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HomeMIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018Lecture 35: Finding Clusters in Graphs
MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018

Lecture 35: Finding Clusters in Graphs

34:49

Description

The topic of this lecture is clustering for graphs, meaning finding sets of “related” vertices in graphs. The challenge is finding good algorithms to optimize cluster quality. Professor Strang reviews some possibilities.

Summary

Two ways to separate graph nodes into clusters

  • k-means: Choose clusters, choose centroids, choose clusters, …
  • Fiedler vector: Eigenvector of graph Laplacian: \(+-\) signs give 2 clusters

Related sections in textbook: IV.6–IV.7

Instructor: Prof. Gilbert Strang